Decision making in many industries and in many specific use cases is often based on uncertain information. For example, in financial applications, a value at risk, which results from complex mathematical calculations based on an assessment of the uncertainty of input data, is calculated prior to investment decisions. Likewise, for multi-million dollar investment decisions, the oil and gas industry relies on the generation of simulations or models of underground geological formations and spatial distributions. Such simulations are based on seismic interpretations, well logs, well tests, etc.
In general, visualization software assists decision makers in their task by providing simulations based on complex mathematical concepts. The results of the simulations are presented to the decision makers through some visualization of the most relevant parameters. The type of visualization varies widely across industries and applications. It can range from simple pie-charts and bar-charts in financial applications, to complex immersive three dimensional (3D) representations of the underground for the petroleum industry, or to virtual reality flythrough of 3D computer aided design (CAD) models of mechanical assemblies.
Regardless of the nature and complexity, visualizations do not generally connote information with complete accuracy. Inaccuracies may for example arise due to flawed modeling techniques as well as incomplete or bad input, such as noisy, poorly sampled, and imprecise data.